Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

Palit, A.K.; Doeding, G.; Anheier, W.; Popović, D.

doi:10.1109/fuzz.2002.1004965

Cited by 12 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“… The weights (w) in layer 4 are updated by [10,11]  The centers and variances of the fuzzy sets in layer 2 are updated by [10,11]…”

Section: Parameters Learning Of Fuzzy Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease

Salim

2015

Jour. Kufa Math. Comp.

View full text Add to dashboard Cite

The aim of this paper is to use Takagi-Sugeno-Kang(zero-order) model as fuzzy neural network for the medical diagnosis of hepatitis diseases which represent a major public health problem all around the world . For further improve the accuracy and the speed of the diagnosis, the Microarray Attribute Reduction Scheme (MARS) for reduction features (or attributes) and Mean Imputation (MI) method for treatment the missing values were used in this work. The used data source of hepatitis diseases was taken from UCI machine learning repository. After treat the missing values problem by apply MI method, the dataset is partitioned into three trainingâ€“testing partitions (30%â€“70%, 40â€“60% and 20%â€“80% respectively) and apply MARS with different values of thr(from 0.1-0.9 ) in order to determine the number attributes (that represent the number of inputs to the fuzzy neural network), the results record in each case of thr values and each case of partitions. The high diagnosis accuracy has been achieved for the 40â€“60% trainingâ€“testing, namely, 100% for training and 95.77% for testing with thr equal to 0.4 and with less training cycle and fuzzy sets number. This work was implemented in MATLAB 7.0 environment. Keywords: medical diagnosis, Fuzzy logic, fuzzy neural network, Microarray Attribute Reduction Scheme, Mean Imputation.

show abstract

“… The weights (w) in layer 4 are updated by [10,11]  The centers and variances of the fuzzy sets in layer 2 are updated by [10,11]…”

Section: Parameters Learning Of Fuzzy Neural Networkmentioning

confidence: 99%

“…The hepatitis database taken from UCI machine learning repository [10] The dataset contains 155 samples.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease

Salim

2015

Jour. Kufa Math. Comp.

View full text Add to dashboard Cite

show abstract

“…There are different training algorithms with different emphasis [11], [32]- [34]. The gradient descent method is the most commonly used in the learning algorithm for feed-forward NNs and fuzzy systems [35]- [41]. As aforementioned, the network architecture of the F-CONFIS is a standard fully connected three layers feedforward NN except that there has repeated link weights between the input and hidden layer.…”

Section: B Special Learning Algorithm For the Fuzzy Neural Network Via A Fully Connected Neural Fuzzy Inference Systemmentioning

confidence: 99%

The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications

Wang

Chen

2014

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

In this paper, a fuzzy neural network (FNN) is transformed into an equivalent three-layer fully connected neural inference system (F-CONFIS). This F-CONFIS is a new type of a neural network whose links are with dependent and repeated weights between the input layer and hidden layer. For these special dependent repeated links of the F-CONFIS, some special properties are revealed. A new learning algorithm with these special properties is proposed in this paper for the F-CONFIS. The F-CONFIS is therefore applied for finding the capacity of the FNN. The lower bound and upper bound of the capacity of the FNN can be found from a new theorem proposed in this paper. Several examples are illustrated with satisfactory simulation results for the capacity of the F-CONFIS (or the FNN). These include "within capacity training of the FNN," "over capacity training of the FNN," "training by increasing the capacity of the FNN," and "impact of the capacity of the FNN in clustering Iris Data." It is noted that the finding of the capacity of the F-CONFIS, or FNN, has its emerging values in all engineering applications using fuzzy neural networks. This is to say that all engineering applications using FNN should not exceed the capacity of the FNN to avoid unexpected results. The clustering of Iris data using FNN illustrated in this paper is one of the most relevant engineering applications in this regards. Index Terms-Capacity of neural networks, fuzzy neural networks (FNNs), fuzzy system, Iris data, neural networks. I. INTRODUCTIONI N the past decade, fuzzy neural networks (FNNs) have been widely used in many kinds of subject areas and engineering applications for problem solving, such as pattern recognition, intelligent adaptive control, regression or density estimation, and so on [1]-[6]. The FNN possesses the characteristics of linguistic information and the learning of a neural Manuscript

show abstract

“…The fuzzy logic system, once represented as the equivalent MultiInput Multi-Output feed forward network, can generally be trained using any suitable training algorithm, such as standard Backpropagation Algorithm (BPA) that is generally used for training of the NN (Palit 2002b . Briefly, the features mentioned above can be put in the updating procedures of LMA and they can be described as follows:…”

Section: Lma Trainingmentioning

confidence: 99%

Inverse Static Analysis of Massive Parallel Arrays of Three- State Actuators via Artificial Intelligence

Pasila

Vertechy

Berselli

et al. 2013

Romansy 19 – Robot Design, Dynamics and Control

View full text Add to dashboard Cite

Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

show abstract

Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

Cited by 12 publications

References 5 publications

Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease

Takagi-Sugeno-Kang(zero-order) model for diagnosis hepatitis disease

The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications

Inverse Static Analysis of Massive Parallel Arrays of Three- State Actuators via Artificial Intelligence

Contact Info

Product

Resources

About